Sudden cardiac death (SCD), usually due to a ventricular tachyarrhythmia (rapid abnormal heart beat), is the most common cause of death in the United States accounting for up to 350,000 deaths per year. Recent clinical trials of patients considered at risk for SCD have demonstrated that the implantable cardioverter defibrillator (ICD) is the most effective therapy currently available. Although the overall mortality benefit from ICD therapy is evident, the magnitude of effectiveness of ICD therapy in clinically defined subgroups is unclear. Clinically, there are numerous unanswered questions related to ICDs and the prevention of SCD. Many of these questions are hoped to be explored through the use of the National ICD registry;others will require new clinical trials, and others may be evaluated through the combination of existing data sources. Formal methods for combining existing data, determining the value of obtaining additional information, comparing the effectiveness of current and novel treatments, and synthesizing evidence to aid clinicians and policymakers in their decision making are needed. Bayesian statistical approaches have been put forward as a method which enables policymakers to harness the power of the available sources of clinical evidence, explore subgroup effects within a trial and across trials in a methodologically rigorous manner, and to assess the uncertainty in clinical trial findings. In addition, these approaches can be incorporated into formalized decision making strategies. Our long-term goal is to enhance the ability of the Agency for Healthcare Research and Quality (AHRQ) to provide evidence-based decision making tools to aid providers and policymakers in the prevention of SCD. To achieve this overall goal, we have four specific aims: (1) To develop a generalizable decision modeling framework for the prevention of SCD;(2) To use Bayesian statistical techniques to devise a model for predicting patient and population health and economic outcomes;(3) To use the framework from Specific Aim 1, the Bayesian model from Specific Aim 2, and patient level data from existing clinical trials, to explore timely clinical and policy questions;and (4) To develop a web-based dissemination system to allow providers and policy makers to interact with the decision modeling framework and to explore clinical and policy questions as evidence evolves. Our research will build off our team's long-term research in chronic disease modeling, Bayesian statistical techniques in clinical trial design/ analysis, methods of disseminating evidence-based decision models to providers and policymakers, and the prevention of SCD. We will collaborate with principal investigators from 11 existing primary and secondary prevention of SCD trials to harness the power of patient level data from over two decades of clinical trials representing 8,200 patients. In an era in which great importance is placed on defending clinical practice with rigorous supporting evidence, our research brings together decision analytic methods, Bayesian statistical techniques, the strength of clinical trial data, and medical informatics tools to provide powerful methods to aid policy makers in their decision making.